Sleep complements wakefulness by supporting an array of cognitive functions. Yet its role in complex behavior such as value-based decision-making remains to be explored. Substantial evidence suggests that the storage of reward-related information does not stay sedentary during sleep. Spontaneous neural activation has been observed in the ventral striatum (Peigneux et al., 2004; Pennartz et al., 2004; Lansink et al., 2008; Lansink et al., 2009) and other brain structures implicated in reward processing (Cantero et al., 2003; Schabus et al., 2007; Singer and Frank, 2009; Fujisawa and Buzsáki, 2011). This engagement of reward circuits has been postulated to promote the consolidation of reward-related information (Pennartz et al., 2004; Fischer and Born, 2009; Pennartz et al., 2011), and prioritize information of high motivational saliency for sleep-dependent reprocessing (Fischer and Born, 2009; Sterpenich et al., 2009; Wilhelm et al., 2011). Some studies also implicate connections of the active reward system in the sleeping brain with abnormal behavior, such as compulsive eating disorders, which have been associated with the dysfunction in reward networks (Perogamvros et al., 2012a). It remains to be explored, however, whether neural activity during sleep directly supports or modulates cognitive processes related to reward processing in ways that affect goal-directed behaviors, despite potential implications of such research for understanding the nature of mechanisms underlying sleep and reward processing and for clinical interventions aiming at modifying human behavior, such as overeating or smoking, to prevent diseases.

A classic model for research in the field of reward processing and value-based behavior entails preference-guided decisions (Rangel et al., 2008). Choosing among different options involves computing a subjective value for each option and comparing these values to make a decision (Samuelson, 1938; Rangel and Hare, 2010). The capacity to effectively evaluate resources and choices is critical for survival and thriving, and the ways of fine-tuning such valuation to reflect subtle changes in the environment or the cognitive state of a decision-maker represent important adaptive systems in animals. Indeed, extant data have demonstrated that subjective preferences are not formed once and forever, but can be modulated by external (e.g. reward reinforcement (Tobler et al., 2005)) and internal factors in the waking state. For example, memory retrieval has been shown to influence the weighting of choice options in certain types of decisions (Barron et al., 2013; Gluth et al., 2015; Bornstein and Norman, 2017), whereas attention affects the comparison of choice values in a range of economic behaviors (Armel et al., 2008; Krajbich et al., 2010; Schonberg et al., 2014). These data thus argue for labile preferences and provide a foundation for investigating the vulnerability of preferences to modulation during sleep.

Here, we explored the extent to which sleep-dependent neurocognitive processing affects the evaluation of intrinsic preferences that guide decisions while awake. In particular, we investigated whether preferences for choice options can be specifically targeted and modified during midday nap, using targeted memory reactivation (TMR), a procedure that has been applied to manipulate neurocognitive processing via stimulating the sleeping brain with unobtrusive cues. The method has been shown effective in selectively improving post-sleep performances in a range of cognitive tasks, including spatial memory (Rasch et al., 2007; Rudoy et al., 2009; Diekelmann et al., 2011), motor memory (Antony et al., 2012), vocabulary learning (Schreiner and Rasch, 2015; Schreiner and Rasch, 2017; Tamminen et al., 2017), fear extinction (Hauner et al., 2013; He et al., 2015), and the reduction of implicit social biases (Hu et al., 2015), but its influence on value-based decisions has yet to be explored.

Specifically, we investigated the effect of covert, reward-related stimulation to the sleeping brain on preferences and choices in an important class of behavior characterized by simple economic choices (e.g. choosing between apples and oranges). Such behavior is prevalent in human and non-human animals and has served as a major building block for understanding complex goal-directed behaviors (Fehr and Rangel, 2011; Padoa-Schioppa, 2011). Focusing on simple choices also allows us to exploit existing knowledge regarding cognitive mechanisms associated with decision-making, and in particular the known relationship between choice and reaction time and the distinct latent aspects of the decision process characterized by computational frameworks such as sequential sampling (Smith and Ratcliff, 2004; Gold and Shadlen, 2007). This framework thus allows us to explore how stimulating the sleeping brain affects the underlying cognitive processes that give rise to preferences and decisions.

We first examined whether TMR modifies behavior and whether such modification is sleep-dependent. We implemented TMR through presenting the spoken name of familiar snack items repeatedly and unobtrusively during non-rapid-eye-movement (NREM) sleep and found a significant post-sleep preference enhancement for items that had been cued relative to items not externally cued. In stark contrast, we found no significant differences in preferences and choices in a control group who underwent the same treatment but were exposed to verbal stimuli while awake. We then examined the extent to which neurocognitive processing during sleep contributes to post-sleep preference shift. By monitoring brain activity during sleep using electroencephalographic recordings and relating this neural measure to behavioral evidence of preference shifts, we found that cueing-specific preference improvements can be reliably predicted by delta or theta band signals induced by cues during sleep. Finally, we fit the decision and reaction time data observed in simple choices using a well-established sequential sampling model to explore the influence of verbal cueing during sleep on the subsequent decision process.

We tested 92 healthy adults (Materials and methods and Supplementary file 1, Table 1A for demographic information) in a battery of classic simple economic choice experiments in combination with the TMR procedure (Figure 1). Subjects were first introduced to a set of 60 familiar snack items (self-reported familiarity rating = 3.51 ± 0.84, on a 5-point scale, Supplementary file 1, Table 1B), and indicated their willingness-to-pay (WTP) in a Becker-DeGroot-Marschak (BDM) auction (Becker et al., 1964), which served as a measure of the baseline preference for a snack item (henceforth WTP1) (Plassmann et al., 2007; Hare et al., 2008; Schonberg et al., 2014). To establish an association between snacks and verbal cues, the spoken name of each snack (e.g. ‘M and M’s candies’) was delivered over a speaker along with the presentation of the item image on a computer screen during the BDM auction. On the basis of WTP1, we chose eight pairs of snacks for each subject that were matched by baseline preferences and randomly divided into cued and uncued conditions.

Figure 1

Download asset Open asset

During subsequent sleep (sleep group, N = 47), the names of eight snack items (i.e. cued items) were delivered over a speaker, each for ten repetitions, during the stage 2 of non-rapid eye movement (N2) sleep with no sleep disruption (Schreiner and Rasch, 2015; Cairney et al., 2018). After waking, we probed the effect of verbal cueing on preferences by re-evaluating the WTP for all 60 items using the same BDM auction (henceforth WTP2). We also probed changes in choices by performing a binary decision task where subjects needed to select between a pair of snacks that were matched for the baseline WTP but differed by whether they had or had not been named during sleep. As a control, we also measured behavior in a control group where participants underwent the same experimental procedure but, rather than taking a 90 min nap as in the asleep condition, were kept awake for the same duration of time (wake group, N = 45; Materials and methods and Supplementary file 1, Table 1A). Sleep monitoring and staging were performed using standard criteria from polysomnography recordings (Methods).

Verbal cueing during sleep but not wakefulness improves preferences for previously cued items

First, we investigated the effect of verbal cueing on the subjective value of a snack by comparing the paired difference of WTP elicited from the first and second BDM auction (i.e. WTP2-WTP1), which for simplicity we refer to as ‘ΔWTP’. Consistent with previous evidence that WTP reliably reflects the subjective value of a familiar option (Plassmann et al., 2007), the baseline ΔWTP (i.e. ΔWTP for items that had not been cued) was not significantly different from zero in either subject group (all subjects, ΔWTP = 0.11 ± 0.12, t₉₁ = 0.99, p = 0.323; sleep vs. wake, t₉₀ = - 0.97, p = 0.336), suggesting the test-and-retest stability of preferences for snack items examined in our experiment.

Using ΔWTP, we found that there was no significant main effect in ΔWTP between the sleep and wake groups (sleep: 0.29 ± 0.12; wake: 0.32 ± 0.11; main effect in cohort, F_1,90 = 0.02, p = 0.886; Figure 2A). On the other hand, there was a significant within-subject difference associated with cueing, such that previously cued items were associated with higher ΔWTP relative to uncued items (cued: 0.50 ± 0.11; uncued: 0.11 ± 0.12; main effect in cueing condition, F_1,90 = 27.61, p = 9.9 × 10⁻⁷; Figure 2A). Importantly, cue-induced effects on preferences varied between sleep and wake cohorts as predicted: ΔWTP was significantly higher for cued relative to uncued items in the sleep group but not in the wake group ((cued vs. uncued) × (sleep vs. wake): F_1,90 = 6.95, p = 0.01; Figure 2A), suggesting that verbal cueing during sleep but not wakefulness promotes preferences for previously cued items. This interaction was significant when analyzed using permutation tests that do not require specific distributional assumptions (Figure 2—figure supplement 1). The result was also robust after taking into account of filler items (Figure 2—figure supplement 2), demographic variables, and changes in self-reported measures of hunger and vigilance before and after the verbal cueing session (Supplementary file 1, Table 1C).

Figure 2 with 2 supplements see all

Download asset Open asset

Verbal cueing during sleep does not influence choice randomness at either the subject or item level

To evaluate the possibility that, rather than preference shifts, the observed choice difference between the sleep and wake groups could be attributed to alternative factors such as changes in choice randomness, we compared the extent to which decisions were guided by value differences between the two participant groups. Specifically, we regressed the likelihood that subjects chose cued over uncued items against the difference in WTP2 (i.e. WTP2_cued -WTP2_uncued; Figure 3A). Consistent with previous studies (Sugrue et al., 2005), WTP2 differences predicted the choice behavior in both sleep and wake groups (sleep: r = 0.37, p = 0.011; wake: r = 0.39, p = 0.009), whereas WTP1 differences had no predictive power for the choice behavior in either subject group (sleep: r = 0.01, p = 0.945; wake: r = 0.05, p = 0.746). Importantly, if changes in choice randomness contribute to the observed behavioral bias under the sleep protocol, we would expect a steeper regression slope in sleep group participants relative to that of the wake group. In contrast to this prediction, the regression results showed no significant differences in slopes between the sleep and wake cohorts (sleep = 0.06; wake = 0.08; F_1,88 = 0.19, p = 0.665). Differences were found only in the intercepts (sleep = 0.56, wake = 0.51, F_1,88 = 6.34, p = 0.014; Figure 3A), suggesting that the behavioral differences between sleep and wake protocols could not be attributed to differential choice randomness but rather to an overall shift in choice propensity toward cued items under the sleep protocol. Similar results were obtained when examining the relationship between choices and WTP biases at the item level (Figure 3B).

Figure 3

Download asset Open asset

Cue-induced delta and theta band power predicts a post-sleep preference shift at both the subject and item level

Next, we investigated how the sleeping brain processed the spoken name of a snack and how such processing contributed to post-sleep behavior using event-related potential (ERP) data measured in a subset of sleep group participants (N = 23; Supplementary file 1, Table 1D, Table 1E; Materials and methods). By averaging ERP amplitudes measured over the interval from −200 to 1800 ms with respect to the onset of each verbal cue, we observed a cue-evoked neural response at the frontal electrode sites (representative electrode F3) during N2 sleep (Figure 4A). Characterized by a positive ERP around 450 ms followed by a negative peak around 1000 ms, this observed pattern is similar to the well-established waveform of K-complexes (KCs) (Figure 4—figure supplement 1A and Supplementary file 1, Table 1F) (Niiyama et al., 1995; Cote et al., 1999; Blume et al., 2017), an electroencephalogram (EEG) graphoelement that has been repeatedly associated with the processing of sensory stimuli, including auditory cues, mainly during periods identified as N2 (Halász, 2005; Cash et al., 2009).

Figure 4 with 5 supplements see all

Download asset Open asset

We then examined how verbal cueing evoked power changes at different frequency bands by analyzing event-related changes in spectral power (ERSPs) before and after the cue onset. For each stimulus presentation, we segmented trials from −800 to 1800 ms with respect to the cue onset (Ruch et al., 2014; Schreiner and Rasch, 2015). Using the mean spectral power measured over the interval from −800 to −200 ms as a baseline, we computed the event-related change relative to the baseline from 0 to 1800 ms (Schreiner and Rasch, 2015). We observed no significant increase in high-frequency activity (8–35 Hz), which would have been observed if verbal stimuli had reduced the depth of sleep in participants (Figure 4A) (Ruch et al., 2014). More specifically, by contrasting the mean spectral power for high-frequency bands before and after the stimulus presentation, we found no significant change for the alpha, beta, or gamma bands (alpha: t₂₂ = - 1.51, p = 0.728; beta: t₂₂ = - 1.68, p = 0.535; gamma: t₂₂ = - 0.23, p = 1; all Bonferroni corrected). In sharp contrast, presentation of verbal stimuli elicited strong event-related synchronization for low-frequency activity (0.5–8 Hz), including the delta band (t₂₂ = - 6.88, p = 3.3 × 10⁻⁶) (Figure 4—figure supplement 1B), which has been previously implicated in the detection of motivationally salient stimuli (Blume et al., 2017) and improved memory consolidation (Oudiette et al., 2013; Batterink et al., 2016), and the theta band (t₂₂ = - 7.13, p = 1.9 × 10⁻⁶), which has been associated with memory processing during wakefulness (Lisman and Jensen, 2013) and successful cueing during non-rapid eye movement sleep in both healthy (Schreiner and Rasch, 2015) and patient (Hot et al., 2011) populations.

Importantly, the cue-induced change in power of the delta and theta bands strongly predicted the degree of cueing-specific preference enhancement after waking, at both the individual and item level. That is, among the sleep group subjects, individuals associated with more prominent increases in average delta or theta band power later demonstrated more pronounced post-sleep preference enhancements for cued items (delta: r = 0.65, p = 0.005; theta: r = 0.64, p = 0.004; all Bonferroni corrected; Figure 4B–C). Similarly, among items that had been presented during sleep, those associated with more prominent increases in average delta or theta band power showed greater post-sleep improvement in WTP (delta: r = 0.52, p = 0.001; theta: r = 0.47, p = 0.005; all Bonferroni corrected; Figure 4D–E). In contrast, there was no correlation between the post-sleep change in preference for cued items and the changes in power in the alpha, beta, or gamma bands (alpha: p = 0.463; beta: p = 1; gamma: p = 1; Bonferroni corrected; Supplementary file 1, Table 1G). Similarly, there was no correlation between the preference change for uncued items and the power change in either the high or low-frequency bands at either the subject or item level (alpha: p = 0.229; beta: p = 0.877; gamma: p = 1; Bonferroni corrected). These results suggest that preference modification following sleep TMR results from neurocognitive processing of verbal stimuli during sleep, as indexed by the power of low-frequency bands evoked by verbal cues.

Combining TMR with classic economic paradigms, the present study demonstrates an intriguing connection between sleep and subjective preferences in an important class of decisions widely used for investigating goal-directed behavior. In particular, the study provides behavioral, neural, and modeling evidence indicating that covert cueing during midday nap can alter preferences and choices in a selective manner, independent of more general effects on decision-making such as hunger, vigilance, or choice randomness. The fact that the stimulation effect is sleep-dependent and that cueing in the waking state produces no significant changes in preferences or choices is consistent with past evidence that selective memory reactivation during wakefulness does not always improve memory (Rudoy et al., 2009; Antony et al., 2012; Schreiner and Rasch, 2015) and sometimes even produces opposing consequences (Diekelmann et al., 2011). Importantly, and consistent with previous studies (Antony et al., 2012; Oudiette et al., 2013; Schreiner and Rasch, 2015), the degree of post-sleep preference enhancement can be predicted by cue-induced increases of theta or delta power, which have been previously associated with successful cueing during sleep (Schreiner et al., 2015; Schreiner and Rasch, 2015) and with improved memory consolidation (Oudiette et al., 2013; Batterink et al., 2016), respectively.

Results of the current study provide novel insights into the nature of cognitive processes that support value evaluation. Our findings corroborate a wealth of evidence suggesting that subjective preferences interact dynamically with the environmental and cognitive states of a decision-maker, and extend these previous findings by demonstrating that sleep also contributes to the dynamic nature of preferences in a flexible, selective manner, such that preferences for specific options can be individually biased during sleep.

In particular, our results are consistent with two broad accounts previously proposed for how preferences could be dynamically modulated. The first involves the possibility that preferences are informed by memory, such that weighting a choice option entails retrieving relevant information from memory at the time of decision-making (Pennartz et al., 2011; Bornstein et al., 2017). Under this interpretation, preference reflects the outcome of memory retrieval, and verbal cueing during sleep encourages the preference of a familiar, valued item by reactivating and strengthening the relevant memories. For example, sleep TMR may trigger the reactivation of reward-related information associated with the cued snack or assist the consolidation of past episodes, such as purchasing or consuming the cued item. In keeping with this possibility, previous studies have suggested that important memories, including those of motivational salience, are preferentially reactivated during sleep (Lansink et al., 2008; Dunsmoor et al., 2015; Blume et al., 2017) and that successful retrieval of a relevant episodic memory (e.g. a past action (Bornstein et al., 2017) or contextual information associated with an action (Bornstein and Norman, 2017)) leads to subsequent decision biases. Neurally, this possibility is consistent with evidence supporting the interplay between memory and choice processes, which involves a network of brain regions, including the hippocampal region, in the processing of memory-related value signals (Barron et al., 2013; Gluth et al., 2015; Palombo et al., 2015; Weilbächer and Gluth, 2016).

At the computational level, this interpretation is consistent with the proposal that connects preference-guided choices and mnemonic processes under the framework of sequential sampling (Gluth et al., 2015; Shadlen and Shohamy, 2016). Interestingly, using LBA, a variant of sequential sampling model, we found that sleep TMR selectively accelerated the accumulation of evidence supporting cued items as measured by drift rate. Past theoretical and empirical work has suggested that the drift rate in sequential sampling models reflects the quality of information drawn from stimuli during the evidence accumulation (Ratcliff and McKoon, 2008; Ratcliff et al., 2016). Within the domain of memory, a higher drift rate is associated with recognizing an item that has been previously presented multiple times relative to an item presented only once (Ratcliff and McKoon, 2008; Gluth et al., 2015). The correlation between drift rate biases and WTP biases observed in our data thus indicates the intriguing possibility that memory that is selectively reactivated during sleep renders higher quality evidence signals in favor of the cued item, giving rise to behavioral shift toward items previously named during sleep. Future work employing various process models (Forstmann et al., 2016) will be needed to further connect behavioral shifts with measures of memory change before and after TMR to investigate which memory is reactivated (e.g. episodic, semantic, emotional) and how different types of memories differentially affect the choice process.

A second but not mutually exclusive possibility is that rather than playing a direct role in affecting value computation and comparison, verbal cueing initiates a chain of events that exerts modulatory effects on the decision process. For example, the behavioral shift observed in our data may reflect the modulatory effect of memory on attention, which in turn biases preferences and choices at the time of decision-making. In line with this hypothesis, recent evidence based on either eye fixation (Krajbich et al., 2010) or cue-approach training (Schonberg et al., 2014; Bakkour et al., 2017) has suggested the influence of attention on decisions. Consequently, if individuals pay more attention to better-remembered options, preference enhancement following selective memory reactivation during sleep may reflect an increase in attention due to improved memory consolidation (Chun and Turk-Browne, 2007; Goldfarb et al., 2016; Rosen et al., 2016). Under this hypothesis, the drift rate identified in the computational model may be associated with attention bias between cued and uncued items, and possibly scaled with the degree to which attention is affected by memory after verbal cueing. Future work incorporating behavioral probes for changes in attention before and after verbal cueing is needed to test and discriminate between these possibilities. Additional experiments may also include neuroimaging studies to identify neural correlates and functional coupling in brain regions previously implicated in attention, memory, and reward processing.

The results of the present study suggest that sleep likely represents a unique period during which preferences and choices that are otherwise stable can be selectively modified by external cues. This finding is consistent with past evidence that targeted memory reactivation not only benefits memories of newly acquired information but can also be used to eliminate long-standing social biases (Hu et al., 2015). It remains to be elucidated whether the findings of the present study are specific to N2 or are generalizable to other stages of sleep, in particular, slow-wave sleep, the primary focus of previous TMR studies. On the one hand, there is evidence for the capacity of neurocognitive processing of verbal stimuli during N2, including extracting information from incoming words (Kouider et al., 2014), differentiating their motivational saliency (Blume et al., 2017), and reactivating the existing motor or vocabulary memories associated with the words (Kouider et al., 2014; Schreiner and Rasch, 2015). Accordingly, one might expect that N2 may be particularly suitable for implementing TMR using verbal stimuli, similar to those employed in our study. On the other hand, it is also possible that our results are not restricted to the period of light sleep, given the wealth of evidence on TMR during slow-wave sleep in humans (Rasch et al., 2007; Rudoy et al., 2009; Antony et al., 2012) and the entire non-rapid eye movement sleep in rodents (Rolls et al., 2013; Barnes and Wilson, 2014). Future studies examining night sleep as opposed to daytime naps or involving deprivation of particular sleep stages are needed to establish the specificity of the observed effects for different sleep stages.

Our results also raise intriguing questions regarding whether the preference enhancement is specific to cued memory reactivation, or hold more generally for both cued and spontaneous reactivation during sleep. According to the active system consolidation model, sleep-dependent memory consolidation critically relies upon repeated reactivation of memory representations (Diekelmann and Born, 2010; Rasch and Born, 2013). It is therefore possible that the amount of memory reactivation taking place during sleep modulates preference improvement. TMR likely plays a role of triggering and enhancing such reactivation, thereby magnifying the influence on preference in the sleeping brain. Under this hypothesis, memory reactivation, either cued or spontaneous, contributes to the fine-tuning of reward evaluation. Yet unlike cued reactivation, which is predicted to bias the weighting of specific choice option likely through its selective influence on memory traces, spontaneous reactivation may exert a less discriminative impact on valuation. For example, there is evidence that, following overnight sleep, subjects demonstrate an overall increase in favorable perception of the whole choice set, due to enhanced recalls for positive information associated with choice options (Karmarkar et al., 2017). In the present study, however, midday nap produces no general effect on either the overt behavior or computational parameters reflecting the latent cognitive processing directly related to decision. One possibility is that the beneficial effect of sleep on memory consolidation requires sufficient sleep duration and the presence of slow-wave sleep (Backhaus and Junghanns, 2006; Diekelmann et al., 2011; Diekelmann et al., 2012), whereas daytime naps usually consist of lighter sleep stages such as stage 1 and 2 (Backhaus and Junghanns, 2006; Tucker et al., 2006; Genzel et al., 2014). TMR during nap in our experiment may serve to elicit memory reactivation that typically occurs during a longer sleep period. Consistent with this interpretation, previous research shows similar effects of memory stabilization following either 40-min sleep with TMR or 90-min sleep without external stimulation (Diekelmann et al., 2012). Future investigations using night sleep or animal models will be needed to assess whether and how spontaneous memory reactivation affects the storage of reward-related information and to determine whether sleep plays a general role in modulating goal-directed behavior through tuning reward evaluation.

More broadly, simple choices are fundamental for goal-directed behavior and have served as a cornerstone for studying reward and motivational control across species (Fehr and Rangel, 2011; Padoa-Schioppa, 2011). Results of this study provide novel insights into the link between sleep and preferences elicited by simple choices, thereby pointing to the possibility of shaping a range of behavior through nudging human sleep. Our results, together with previous findings on sleep-dependent reward-related neural activation (Pennartz et al., 2004; Lansink et al., 2008; Lansink et al., 2009; Perogamvros and Schwartz, 2012b; Igloi et al., 2015), raise the intriguing question regarding whether, and under what circumstances, value assessment and comparison can be perturbed by external cues while asleep. Future studies are needed to address whether our findings generalize to other types of preferences, including those involving risk and ambiguity (Hsu et al., 2005), temporal discounting (McClure et al., 2004), loss aversion (Tom et al., 2007), and prosocial considerations (Hein et al., 2016).

Data and code used for data analysis are publicly available online via Open Science Framework (OSF) at (https://osf.io/9ndhy/).

1. 1. Yin Y
   2. Ai S

   (2018) Open Science Framework

   Data and Code for Promoting subjective preferences in simple economic choices during nap.

   https://doi.org/10.17605/OSF.IO/9NDHY

1. 1. Ai SZ
   2. Chen J
   3. Liu JF
   4. He J
   5. Xue YX
   6. Bao YP
   7. Han F
   8. Tang XD
   9. Lu L
   10. Shi J

   (2015) Exposure to extinction-associated contextual tone during slow-wave sleep and wakefulness differentially modulates fear expression

   Neurobiology of Learning and Memory 123:159–167.

   https://doi.org/10.1016/j.nlm.2015.06.005

   • PubMed
   • Google Scholar
2. 1. Antony JW
   2. Gobel EW
   3. O'Hare JK
   4. Reber PJ
   5. Paller KA

   (2012) Cued memory reactivation during sleep influences skill learning

   Nature Neuroscience 15:1114–1116.

   https://doi.org/10.1038/nn.3152

   • PubMed
   • Google Scholar
3. 1. Armel KC
   2. Beaumel A
   3. Rangel A

   (2008)

   Biasing simple choices by manipulating relative visual attention

   Judgment and Decision Making 3:396–403.

   • Google Scholar
4. 1. Backhaus J
   2. Junghanns K

   (2006) Daytime naps improve procedural motor memory

   Sleep Medicine 7:508–512.

   https://doi.org/10.1016/j.sleep.2006.04.002

   • PubMed
   • Google Scholar
5. 1. Bakkour A
   2. Lewis-Peacock JA
   3. Poldrack RA
   4. Schonberg T

   (2017) Neural mechanisms of cue-approach training

   NeuroImage 151:92–104.

   https://doi.org/10.1016/j.neuroimage.2016.09.059

   • Google Scholar
6. 1. Barnes DC
   2. Wilson DA

   (2014) Slow-wave sleep-imposed replay modulates both strength and precision of memory

   Journal of Neuroscience 34:5134–5142.

   https://doi.org/10.1523/JNEUROSCI.5274-13.2014

   • PubMed
   • Google Scholar
7. 1. Barron HC
   2. Dolan RJ
   3. Behrens TE

   (2013) Online evaluation of novel choices by simultaneous representation of multiple memories

   Nature Neuroscience 16:1492–1498.

   https://doi.org/10.1038/nn.3515

   • PubMed
   • Google Scholar
8. 1. Batterink LJ
   2. Creery JD
   3. Paller KA

   (2016) Phase of Spontaneous Slow Oscillations during Sleep Influences Memory-Related Processing of Auditory Cues

   Journal of Neuroscience 36:1401–1409.

   https://doi.org/10.1523/JNEUROSCI.3175-15.2016

   • PubMed
   • Google Scholar
9. 1. Becker GM
   2. DeGroot MH
   3. Marschak J

   (1964) Measuring utility by a single-response sequential method

   Behavioral Science 9:226–232.

   https://doi.org/10.1002/bs.3830090304

   • PubMed
   • Google Scholar
10. 1. Blume C
    2. Del Giudice R
    3. Lechinger J
    4. Wislowska M
    5. Heib DPJ
    6. Hoedlmoser K
    7. Schabus M

    (2017) Preferential processing of emotionally and self-relevant stimuli persists in unconscious N2 sleep

    Brain and Language 167:72–82.

    https://doi.org/10.1016/j.bandl.2016.02.004

    • PubMed
    • Google Scholar
11. 1. Bornstein AM
    2. Khaw MW
    3. Shohamy D
    4. Daw ND

    (2017) Reminders of past choices bias decisions for reward in humans

    Nature Communications 8:15958.

    https://doi.org/10.1038/ncomms15958

    • PubMed
    • Google Scholar
12. 1. Bornstein AM
    2. Norman KA

    (2017) Reinstated episodic context guides sampling-based decisions for reward

    Nature Neuroscience 20:997–1003.

    https://doi.org/10.1038/nn.4573

    • PubMed
    • Google Scholar
13. 1. Brainard DH

    (1997) The Psychophysics Toolbox

    Spatial Vision 10:433–436.

    https://doi.org/10.1163/156856897X00357

    • PubMed
    • Google Scholar
14. 1. Brown SD
    2. Heathcote A

    (2008) The simplest complete model of choice response time: linear ballistic accumulation

    Cognitive Psychology 57:153–178.

    https://doi.org/10.1016/j.cogpsych.2007.12.002

    • PubMed
    • Google Scholar
15. 1. Cairney SA
    2. Guttesen AÁV
    3. El Marj N
    4. Staresina BP

    (2018) Memory Consolidation Is Linked to Spindle-Mediated Information Processing during Sleep

    Current Biology 28:948–954.

    https://doi.org/10.1016/j.cub.2018.01.087

    • PubMed
    • Google Scholar
16. 1. Cantero JL
    2. Atienza M
    3. Stickgold R
    4. Kahana MJ
    5. Madsen JR
    6. Kocsis B

    (2003) Sleep-Dependent θ Oscillations in the Human Hippocampus and Neocortex

    The Journal of Neuroscience 23:10897–10903.

    https://doi.org/10.1523/JNEUROSCI.23-34-10897.2003

    • Google Scholar
17. 1. Carpenter B
    2. Gelman A
    3. Hoffman MD
    4. Lee D
    5. Goodrich B
    6. Betancourt M
    7. Brubaker M
    8. Guo J
    9. Li P
    10. Riddell A

    (2017) Stan : A Probabilistic Programming Language

    Journal of Statistical Software 76:.

    https://doi.org/10.18637/jss.v076.i01

    • Google Scholar
18. 1. Cash SS
    2. Halgren E
    3. Dehghani N
    4. Rossetti AO
    5. Thesen T
    6. Wang C
    7. Devinsky O
    8. Kuzniecky R
    9. Doyle W
    10. Madsen JR
    11. Bromfield E
    12. Eross L
    13. Halász P
    14. Karmos G
    15. Csercsa R
    16. Wittner L
    17. Ulbert I

    (2009) The human K-complex represents an isolated cortical down-state

    Science 324:1084–1087.

    https://doi.org/10.1126/science.1169626

    • PubMed
    • Google Scholar
19. 1. Chun MM
    2. Turk-Browne NB

    (2007) Interactions between attention and memory

    Current Opinion in Neurobiology 17:177–184.

    https://doi.org/10.1016/j.conb.2007.03.005

    • PubMed
    • Google Scholar
20. 1. Cote KA
    2. de Lugt DR
    3. Langley SD
    4. Campbell KB

    (1999) Scalp topography of the auditory evoked K-complex in stage 2 and slow wave sleep

    Journal of Sleep Research 8:263–272.

    https://doi.org/10.1046/j.1365-2869.1999.00164.x

    • PubMed
    • Google Scholar
21. 1. Delorme A
    2. Makeig S

    (2004) EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis

    Journal of Neuroscience Methods 134:9–21.

    https://doi.org/10.1016/j.jneumeth.2003.10.009

    • PubMed
    • Google Scholar
22. 1. Diekelmann S
    2. Biggel S
    3. Rasch B
    4. Born J

    (2012) Offline consolidation of memory varies with time in slow wave sleep and can be accelerated by cuing memory reactivations

    Neurobiology of Learning and Memory 98:103–111.

    https://doi.org/10.1016/j.nlm.2012.07.002

    • PubMed
    • Google Scholar
23. 1. Diekelmann S
    2. Born J

    (2010) The memory function of sleep

    Nature Reviews Neuroscience 11:114–126.

    https://doi.org/10.1038/nrn2762

    • PubMed
    • Google Scholar
24. 1. Diekelmann S
    2. Büchel C
    3. Born J
    4. Rasch B

    (2011) Labile or stable: opposing consequences for memory when reactivated during waking and sleep

    Nature Neuroscience 14:381–386.

    https://doi.org/10.1038/nn.2744

    • PubMed
    • Google Scholar
25. 1. Dunsmoor JE
    2. Murty VP
    3. Davachi L
    4. Phelps EA

    (2015) Emotional learning selectively and retroactively strengthens memories for related events

    Nature 520:345–348.

    https://doi.org/10.1038/nature14106

    • PubMed
    • Google Scholar
26. 1. Fehr E
    2. Rangel A

    (2011) Neuroeconomic Foundations of Economic Choice—Recent Advances

    Journal of Economic Perspectives 25:3–30.

    https://doi.org/10.1257/jep.25.4.3

    • Google Scholar
27. 1. Fischer S
    2. Born J

    (2009) Anticipated reward enhances offline learning during sleep

    Journal of Experimental Psychology: Learning, Memory, and Cognition 35:1586–1593.

    https://doi.org/10.1037/a0017256

    • Google Scholar
28. 1. Forstmann BU
    2. Dutilh G
    3. Brown S
    4. Neumann J
    5. von Cramon DY
    6. Ridderinkhof KR
    7. Wagenmakers EJ

    (2008) Striatum and pre-SMA facilitate decision-making under time pressure

    PNAS 105:17538–17542.

    https://doi.org/10.1073/pnas.0805903105

    • PubMed
    • Google Scholar
29. 1. Forstmann BU
    2. Anwander A
    3. Schäfer A
    4. Neumann J
    5. Brown S
    6. Wagenmakers EJ
    7. Bogacz R
    8. Turner R

    (2010) Cortico-striatal connections predict control over speed and accuracy in perceptual decision making

    PNAS 107:15916–15920.

    https://doi.org/10.1073/pnas.1004932107

    • PubMed
    • Google Scholar
30. 1. Forstmann BU
    2. Ratcliff R
    3. Wagenmakers EJ

    (2016) Sequential Sampling Models in Cognitive Neuroscience: Advantages, Applications, and Extensions

    Annual Review of Psychology 67:641–666.

    https://doi.org/10.1146/annurev-psych-122414-033645

    • PubMed
    • Google Scholar
31. 1. Fujisawa S
    2. Buzsáki G

    (2011) A 4 Hz oscillation adaptively synchronizes prefrontal, VTA, and hippocampal activities

    Neuron 72:153–165.

    https://doi.org/10.1016/j.neuron.2011.08.018

    • PubMed
    • Google Scholar
32. Book

    1. Gelman A
    2. Carlin JB
    3. Stern HS
    4. Dunson DB
    5. Vehtari A
    6. Rubin DB

    (2014)

    Bayesian Data Analysis (Third Edition)

    England: Taylor & Francis.

    • Google Scholar
33. 1. Genzel L
    2. Kroes MC
    3. Dresler M
    4. Battaglia FP

    (2014) Light sleep versus slow wave sleep in memory consolidation: a question of global versus local processes?

    Trends in Neurosciences 37:10–19.

    https://doi.org/10.1016/j.tins.2013.10.002

    • PubMed
    • Google Scholar
34. 1. Gluth S
    2. Sommer T
    3. Rieskamp J
    4. Büchel C

    (2015) Effective Connectivity between Hippocampus and Ventromedial Prefrontal Cortex Controls Preferential Choices from Memory

    Neuron 86:1078–1090.

    https://doi.org/10.1016/j.neuron.2015.04.023

    • PubMed
    • Google Scholar
35. 1. Gold JI
    2. Shadlen MN

    (2007) The neural basis of decision making

    Annual Review of Neuroscience 30:535–574.

    https://doi.org/10.1146/annurev.neuro.29.051605.113038

    • PubMed
    • Google Scholar
36. 1. Goldfarb EV
    2. Chun MM
    3. Phelps EA

    (2016) Memory-Guided Attention: Independent Contributions of the Hippocampus and Striatum

    Neuron 89:317–324.

    https://doi.org/10.1016/j.neuron.2015.12.014

    • Google Scholar
37. 1. Halász P

    (2005) K-complex, a reactive EEG graphoelement of NREM sleep: an old chap in a new garment

    Sleep Medicine Reviews 9:391–412.

    https://doi.org/10.1016/j.smrv.2005.04.003

    • PubMed
    • Google Scholar
38. 1. Hare TA
    2. O'Doherty J
    3. Camerer CF
    4. Schultz W
    5. Rangel A

    (2008) Dissociating the role of the orbitofrontal cortex and the striatum in the computation of goal values and prediction errors

    Journal of Neuroscience 28:5623–5630.

    https://doi.org/10.1523/JNEUROSCI.1309-08.2008

    • PubMed
    • Google Scholar
39. 1. Hauner KK
    2. Howard JD
    3. Zelano C
    4. Gottfried JA

    (2013) Stimulus-specific enhancement of fear extinction during slow-wave sleep

    Nature Neuroscience 16:1553–1555.

    https://doi.org/10.1038/nn.3527

    • PubMed
    • Google Scholar
40. 1. Hawkins GE
    2. Hayes BK
    3. Heit E

    (2016) A dynamic model of reasoning and memory

    Journal of Experimental Psychology: General 145:155–180.

    https://doi.org/10.1037/xge0000113

    • Google Scholar
41. 1. He J
    2. Sun HQ
    3. Li SX
    4. Zhang WH
    5. Shi J
    6. Ai SZ
    7. Li Y
    8. Li XJ
    9. Tang XD
    10. Lu L

    (2015) Effect of conditioned stimulus exposure during slow wave sleep on fear memory extinction in humans

    Sleep 38:423–431.

    https://doi.org/10.5665/sleep.4502

    • PubMed
    • Google Scholar
42. 1. Hein G
    2. Morishima Y
    3. Leiberg S
    4. Sul S
    5. Fehr E

    (2016) The brain's functional network architecture reveals human motives

    Science 351:1074–1078.

    https://doi.org/10.1126/science.aac7992

    • PubMed
    • Google Scholar
43. 1. Hot P
    2. Rauchs G
    3. Bertran F
    4. Denise P
    5. Desgranges B
    6. Clochon P
    7. Eustache F

    (2011) Changes in sleep theta rhythm are related to episodic memory impairment in early Alzheimer's disease

    Biological Psychology 87:334–339.

    https://doi.org/10.1016/j.biopsycho.2011.04.002

    • PubMed
    • Google Scholar
44. 1. Hsu M
    2. Bhatt M
    3. Adolphs R
    4. Tranel D
    5. Camerer CF

    (2005) Neural systems responding to degrees of uncertainty in human decision-making

    Science 310:1680–1683.

    https://doi.org/10.1126/science.1115327

    • PubMed
    • Google Scholar
45. 1. Hu X
    2. Antony JW
    3. Creery JD
    4. Vargas IM
    5. Bodenhausen GV
    6. Paller KA

    (2015) Unlearning implicit social biases during sleep

    Science 348:1013–1015.

    https://doi.org/10.1126/science.aaa3841

    • Google Scholar
46. Book

    1. Iber C
    2. Ancoli-Israel S
    3. Chesson A
    4. Quan S

    (2007)

    The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications

    Westchester, IL: American Academy of Sleep Medicine.

    • Google Scholar
47. 1. Igloi K
    2. Gaggioni G
    3. Sterpenich V
    4. Schwartz S

    (2015) A nap to recap or how reward regulates hippocampal-prefrontal memory networks during daytime sleep in humans

    eLife 4:e07903.

    https://doi.org/10.7554/eLife.07903

    • PubMed
    • Google Scholar
48. 1. Karmarkar UR
    2. Shiv B
    3. Spencer RMC

    (2017) Should you Sleep on it? The Effects of Overnight Sleep on Subjective Preference-based Choice

    Journal of Behavioral Decision Making 30:70–79.

    https://doi.org/10.1002/bdm.1921

    • Google Scholar
49. 1. Kouider S
    2. Andrillon T
    3. Barbosa LS
    4. Goupil L
    5. Bekinschtein TA

    (2014) Inducing task-relevant responses to speech in the sleeping brain

    Current Biology 24:2208–2214.

    https://doi.org/10.1016/j.cub.2014.08.016

    • PubMed
    • Google Scholar
50. 1. Krajbich I
    2. Armel C
    3. Rangel A

    (2010) Visual fixations and the computation and comparison of value in simple choice

    Nature Neuroscience 13:1292–1298.

    https://doi.org/10.1038/nn.2635

    • PubMed
    • Google Scholar
51. 1. Kruschke JK

    (2010) What to believe: Bayesian methods for data analysis

    Trends in Cognitive Sciences 14:293–300.

    https://doi.org/10.1016/j.tics.2010.05.001

    • PubMed
    • Google Scholar
52. 1. Lansink CS
    2. Goltstein PM
    3. Lankelma JV
    4. Joosten RN
    5. McNaughton BL
    6. Pennartz CM

    (2008) Preferential reactivation of motivationally relevant information in the ventral striatum

    Journal of Neuroscience 28:6372–6382.

    https://doi.org/10.1523/JNEUROSCI.1054-08.2008

    • PubMed
    • Google Scholar
53. 1. Lansink CS
    2. Goltstein PM
    3. Lankelma JV
    4. McNaughton BL
    5. Pennartz CM

    (2009) Hippocampus leads ventral striatum in replay of place-reward information

    PLoS Biology 7:e1000173.

    https://doi.org/10.1371/journal.pbio.1000173

    • PubMed
    • Google Scholar
54. Book

    1. Lee MD
    2. Wagenmakers EJ

    (2014)

    Bayesian Cognitive Modeling: A Practical Course

    Cambridge University Press.

    • Google Scholar
55. 1. Lisman JE
    2. Jensen O

    (2013) The Theta-Gamma Neural Code

    Neuron 77:1002–1016.

    https://doi.org/10.1016/j.neuron.2013.03.007

    • Google Scholar
56. 1. McClure SM
    2. Laibson DI
    3. Loewenstein G
    4. Cohen JD

    (2004) Separate neural systems value immediate and delayed monetary rewards

    Science 306:503–507.

    https://doi.org/10.1126/science.1100907

    • PubMed
    • Google Scholar
57. 1. Niiyama Y
    2. Fushimi M
    3. Sekine A
    4. Hishikawa Y

    (1995) K-complex evoked in NREM sleep is accompanied by a slow negative potential related to cognitive process

    Electroencephalography and Clinical Neurophysiology 95:27–33.

    https://doi.org/10.1016/0013-4694(95)00021-P

    • PubMed
    • Google Scholar
58. 1. Oudiette D
    2. Antony JW
    3. Creery JD
    4. Paller KA

    (2013) The role of memory reactivation during wakefulness and sleep in determining which memories endure

    Journal of Neuroscience 33:6672–6678.

    https://doi.org/10.1523/JNEUROSCI.5497-12.2013

    • PubMed
    • Google Scholar
59. 1. Padoa-Schioppa C

    (2011) Neurobiology of economic choice: a good-based model

    Annual Review of Neuroscience 34:333–359.

    https://doi.org/10.1146/annurev-neuro-061010-113648

    • PubMed
    • Google Scholar
60. 1. Palombo DJ
    2. Keane MM
    3. Verfaellie M

    (2015) How does the hippocampus shape decisions?

    Neurobiology of Learning and Memory 125:93–97.

    https://doi.org/10.1016/j.nlm.2015.08.005

    • PubMed
    • Google Scholar
61. 1. Peigneux P
    2. Laureys S
    3. Fuchs S
    4. Collette F
    5. Perrin F
    6. Reggers J
    7. Phillips C
    8. Degueldre C
    9. Del Fiore G
    10. Aerts J
    11. Luxen A
    12. Maquet P

    (2004) Are spatial memories strengthened in the human hippocampus during slow wave sleep?

    Neuron 44:535–545.

    https://doi.org/10.1016/j.neuron.2004.10.007

    • PubMed
    • Google Scholar
62. 1. Pennartz CM
    2. Lee E
    3. Verheul J
    4. Lipa P
    5. Barnes CA
    6. McNaughton BL

    (2004) The ventral striatum in off-line processing: ensemble reactivation during sleep and modulation by hippocampal ripples

    Journal of Neuroscience 24:6446–6456.

    https://doi.org/10.1523/JNEUROSCI.0575-04.2004

    • PubMed
    • Google Scholar
63. 1. Pennartz CM
    2. Ito R
    3. Verschure PF
    4. Battaglia FP
    5. Robbins TW

    (2011) The hippocampal-striatal axis in learning, prediction and goal-directed behavior

    Trends in Neurosciences 34:548–559.

    https://doi.org/10.1016/j.tins.2011.08.001

    • PubMed
    • Google Scholar
64. 1. Perogamvros L
    2. Baud P
    3. Hasler R
    4. Cloninger CR
    5. Schwartz S
    6. Perrig S

    (2012a) Active Reward Processing during Human Sleep: Insights from Sleep-Related Eating Disorder

    Frontiers in Neurology 3:168.

    https://doi.org/10.3389/fneur.2012.00168

    • PubMed
    • Google Scholar
65. 1. Perogamvros L
    2. Schwartz S

    (2012b) The roles of the reward system in sleep and dreaming

    Neuroscience & Biobehavioral Reviews 36:1934–1951.

    https://doi.org/10.1016/j.neubiorev.2012.05.010

    • PubMed
    • Google Scholar
66. 1. Plassmann H
    2. O'Doherty J
    3. Rangel A

    (2007) Orbitofrontal cortex encodes willingness to pay in everyday economic transactions

    Journal of Neuroscience 27:9984–9988.

    https://doi.org/10.1523/JNEUROSCI.2131-07.2007

    • PubMed
    • Google Scholar
67. 1. Rangel A
    2. Camerer C
    3. Montague PR

    (2008) A framework for studying the neurobiology of value-based decision making

    Nature Reviews Neuroscience 9:545–556.

    https://doi.org/10.1038/nrn2357

    • PubMed
    • Google Scholar
68. 1. Rangel A
    2. Hare T

    (2010) Neural computations associated with goal-directed choice

    Current Opinion in Neurobiology 20:262–270.

    https://doi.org/10.1016/j.conb.2010.03.001

    • PubMed
    • Google Scholar
69. 1. Rasch B
    2. Büchel C
    3. Gais S
    4. Born J

    (2007) Odor cues during slow-wave sleep prompt declarative memory consolidation

    Science 315:1426–1429.

    https://doi.org/10.1126/science.1138581

    • PubMed
    • Google Scholar
70. 1. Rasch B
    2. Born J

    (2013) About sleep's role in memory

    Physiological Reviews 93:681–766.

    https://doi.org/10.1152/physrev.00032.2012

    • PubMed
    • Google Scholar
71. 1. Ratcliff R
    2. McKoon G

    (2008) The diffusion decision model: theory and data for two-choice decision tasks

    Neural Computation 20:873–922.

    https://doi.org/10.1162/neco.2008.12-06-420

    • PubMed
    • Google Scholar
72. 1. Ratcliff R
    2. Smith PL
    3. Brown SD
    4. McKoon G

    (2016) Diffusion Decision Model: Current Issues and History

    Trends in Cognitive Sciences 20:260–281.

    https://doi.org/10.1016/j.tics.2016.01.007

    • PubMed
    • Google Scholar
73. 1. Rodriguez CA
    2. Turner BM
    3. Van Zandt T
    4. McClure SM

    (2015) The neural basis of value accumulation in intertemporal choice

    European Journal of Neuroscience 42:2179–2189.

    https://doi.org/10.1111/ejn.12997

    • PubMed
    • Google Scholar
74. 1. Rolls A
    2. Makam M
    3. Kroeger D
    4. Colas D
    5. de Lecea L
    6. Heller HC

    (2013) Sleep to forget: interference of fear memories during sleep

    Molecular Psychiatry 18:1166–1170.

    https://doi.org/10.1038/mp.2013.121

    • PubMed
    • Google Scholar
75. 1. Rosen ML
    2. Stern CE
    3. Michalka SW
    4. Devaney KJ
    5. Somers DC

    (2016) Cognitive Control Network Contributions to Memory-Guided Visual Attention

    Cerebral Cortex 26:2059–2073.

    https://doi.org/10.1093/cercor/bhv028

    • PubMed
    • Google Scholar
76. 1. Ruch S
    2. Koenig T
    3. Mathis J
    4. Roth C
    5. Henke K

    (2014) Word encoding during sleep is suggested by correlations between word-evoked up-states and post-sleep semantic priming

    Frontiers in Psychology 5:1319.

    https://doi.org/10.3389/fpsyg.2014.01319

    • PubMed
    • Google Scholar
77. 1. Rudoy JD
    2. Voss JL
    3. Westerberg CE
    4. Paller KA

    (2009) Strengthening individual memories by reactivating them during sleep

    Science 326:1079.

    https://doi.org/10.1126/science.1179013

    • PubMed
    • Google Scholar
78. 1. Samuelson PA

    (1938) A Note on the Pure Theory of Consumer's Behaviour

    Economica 5:61–71.

    https://doi.org/10.2307/2548836

    • Google Scholar
79. 1. Schabus M
    2. Dang-Vu TT
    3. Albouy G
    4. Balteau E
    5. Boly M
    6. Carrier J
    7. Darsaud A
    8. Degueldre C
    9. Desseilles M
    10. Gais S
    11. Phillips C
    12. Rauchs G
    13. Schnakers C
    14. Sterpenich V
    15. Vandewalle G
    16. Luxen A
    17. Maquet P

    (2007) Hemodynamic cerebral correlates of sleep spindles during human non-rapid eye movement sleep

    PNAS 104:13164–13169.

    https://doi.org/10.1073/pnas.0703084104

    • Google Scholar
80. 1. Schonberg T
    2. Bakkour A
    3. Hover AM
    4. Mumford JA
    5. Nagar L
    6. Perez J
    7. Poldrack RA

    (2014) Changing value through cued approach: an automatic mechanism of behavior change

    Nature Neuroscience 17:625–630.

    https://doi.org/10.1038/nn.3673

    • PubMed
    • Google Scholar
81. 1. Schreiner T
    2. Lehmann M
    3. Rasch B

    (2015) Auditory feedback blocks memory benefits of cueing during sleep

    Nature Communications 6:8729.

    https://doi.org/10.1038/ncomms9729

    • PubMed
    • Google Scholar
82. 1. Schreiner T
    2. Rasch B

    (2015) Boosting Vocabulary Learning by Verbal Cueing During Sleep

    Cerebral Cortex 25:4169–4179.

    https://doi.org/10.1093/cercor/bhu139

    • PubMed
    • Google Scholar
83. 1. Schreiner T
    2. Rasch B

    (2017) The beneficial role of memory reactivation for language learning during sleep: A review

    Brain and Language 167:94–105.

    https://doi.org/10.1016/j.bandl.2016.02.005

    • PubMed
    • Google Scholar
84. 1. Shadlen MN
    2. Shohamy D

    (2016) Decision Making and Sequential Sampling from Memory

    Neuron 90:927–939.

    https://doi.org/10.1016/j.neuron.2016.04.036

    • Google Scholar
85. 1. Singer AC
    2. Frank LM

    (2009) Rewarded outcomes enhance reactivation of experience in the hippocampus

    Neuron 64:910–921.

    https://doi.org/10.1016/j.neuron.2009.11.016

    • PubMed
    • Google Scholar
86. 1. Smith PL
    2. Ratcliff R

    (2004) Psychology and neurobiology of simple decisions

    Trends in Neurosciences 27:161–168.

    https://doi.org/10.1016/j.tins.2004.01.006

    • PubMed
    • Google Scholar
87. 1. Sterpenich V
    2. Albouy G
    3. Darsaud A
    4. Schmidt C
    5. Vandewalle G
    6. Dang Vu TT
    7. Desseilles M
    8. Phillips C
    9. Degueldre C
    10. Balteau E
    11. Collette F
    12. Luxen A
    13. Maquet P

    (2009) Sleep promotes the neural reorganization of remote emotional memory

    Journal of Neuroscience 29:5143–5152.

    https://doi.org/10.1523/JNEUROSCI.0561-09.2009

    • PubMed
    • Google Scholar
88. 1. Sugrue LP
    2. Corrado GS
    3. Newsome WT

    (2005) Choosing the greater of two goods: neural currencies for valuation and decision making

    Nature Reviews Neuroscience 6:363–375.

    https://doi.org/10.1038/nrn1666

    • PubMed
    • Google Scholar
89. 1. Tamminen J
    2. Lambon Ralph MA
    3. Lewis PA

    (2017) Targeted memory reactivation of newly learned words during sleep triggers REM-mediated integration of new memories and existing knowledge

    Neurobiology of Learning and Memory 137:77–82.

    https://doi.org/10.1016/j.nlm.2016.11.012

    • PubMed
    • Google Scholar
90. 1. Tobler PN
    2. Fiorillo CD
    3. Schultz W

    (2005) Adaptive coding of reward value by dopamine neurons

    Science 307:1642–1645.

    https://doi.org/10.1126/science.1105370

    • PubMed
    • Google Scholar
91. 1. Tom SM
    2. Fox CR
    3. Trepel C
    4. Poldrack RA

    (2007) The neural basis of loss aversion in decision-making under risk

    Science 315:515–518.

    https://doi.org/10.1126/science.1134239

    • PubMed
    • Google Scholar
92. 1. Trueblood JS
    2. Brown SD
    3. Heathcote A

    (2014) The multiattribute linear ballistic accumulator model of context effects in multialternative choice

    Psychological Review 121:179–205.

    https://doi.org/10.1037/a0036137

    • PubMed
    • Google Scholar
93. 1. Tucker MA
    2. Hirota Y
    3. Wamsley EJ
    4. Lau H
    5. Chaklader A
    6. Fishbein W

    (2006) A daytime nap containing solely non-REM sleep enhances declarative but not procedural memory

    Neurobiology of Learning and Memory 86:241–247.

    https://doi.org/10.1016/j.nlm.2006.03.005

    • PubMed
    • Google Scholar
94. 1. Weilbächer R
    2. Gluth S

    (2016) The Interplay of Hippocampus and Ventromedial Prefrontal Cortex in Memory-Based Decision Making

    Brain Sciences 7:4.

    https://doi.org/10.3390/brainsci7010004

    • Google Scholar
95. 1. Wilhelm I
    2. Diekelmann S
    3. Molzow I
    4. Ayoub A
    5. Mölle M
    6. Born J

    (2011) Sleep selectively enhances memory expected to be of future relevance

    Journal of Neuroscience 31:1563–1569.

    https://doi.org/10.1523/JNEUROSCI.3575-10.2011

    • PubMed
    • Google Scholar
96. Data

    1. Yin Y
    2. Ai S

    (authors) (2018) Data and Code for 'Promoting Subjective Preferences in Simple Economic Choices during Nap'

    Open Science Framework.

    https://doi.org/10.17605/OSF.IO/9NDHY

Thank you for submitting your article "Promoting subjective preferences in simple economic choices during sleep" for consideration by eLife. Your article has been reviewed by two peer reviewers, including Michael Breakspear as the Reviewing Editors and Reviewer #1, and the evaluation has been overseen by a Reviewing Editor and Michael Frank as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Björn Rasch (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

This is an interesting, well-posed and clearly reported study. The finding that cues provided during sleep influence behaviour according to their neurophysiological correlates is likely to be of substantial interest to sleep researchers and those examining value-based decision making.

The authors examined the effect of target memory reactivation (TMR) during a 90 min midday nap on choice preferences in economic decision paradigms. They show that re-exposure to names of snacks during NREM sleep biased choices towards the snack. In contrast, re-exposure to the snack names during wakefulness did not affect decision-making. In addition, stimulus-evoked δ activity and theta activity predicted changes in choice behavior on the both the subject and item level. The authors conclude that choice preferences might relate to memory processes, which can be biased using TMR techniques during sleep.

The paper and the reported findings are exciting. While the memory benefits of TMR during sleep are well established, the authors show for the first time that also behavior in decision-making paradigms can be biased by influencing memory processing during sleep. The authors use established paradigms and cueing techniques in their study, and the methodological approach is sound. The participant samples are relatively large compared to other TMR studies. A particular strength of the paper is that the authors show that the effect of TMR on decision-making occurs in the sleep, but not in the wake group. Furthermore, the authors provide a wide range of analyses and results, which are all highly informative and important.

Essential revisions:

Both reviewers' concerns are listed separately but note that comment 3 from reviewer 1 is very similar to comment 2 from reviewer 2 and comment 2 by reviewer 1is similar to comment 4 by reviewer 2: it is reasonable for you to collapse your responses to these concerns into two single responses.

Reviewer #1:

1) Were the subjects told they were going to be delivered verbal cues when asleep? Were they aware of the study hypotheses? What was the nature and results of the "post-sleep awareness test" – perhaps move this to the Results section.

2) What was the nature of recruiting so many participants whom regularly napped in the afternoon and duly cooperated and slept in a laboratory setting? This does not seem a typical population to me. How were the non-sleep group kept awake and were they checked for drowsiness during the word cueing?

3) What motivated the decision to multiply N2 by the mean δ and theta amplitudes? This sounds rather exploratory to me (given that each measure alone is close to significant; that several various combinations are possible; and that the final result (p=0.026) would not survive multiple comparisons.

Reviewer #2:

1) As the general results of cueing during sleep appear to be rather robust, it puzzles me that the authors did not find any differences in the drift diffusion model. Is it possible to compare cued vs uncued items in this model, and not sleep vs. wake in general? How do the authors explain that this model is not sensitive for the observed choice preferences? Please discuss this point in more detail in the discussion. In particular, please elaborate on the functional consequences of the null findings in this model.

2) Related to the point above, the reported correlation in Figure 4F (i.e., N2 amount*delta activity and drift rate) appears to be highly exploratory and speculative. With four model parameters and many possible combinations of sleep stage durations and oscillatory activity, it is highly probable that this is a false positive. Did the authors also correct for multiple comparisons here, as in the other analyses? I would strongly suggest omitting this part or at least strongly attenuating the conclusions made from this analysis. For me, the more important point here is that the model parameters are not sensitive for cueing benefits.

3) The authors should give some more details on the reactivation itself: How many cues were delivered on average? How many cues were delivered in N2 vs N3 vs in other sleep stages? How does number of cues relate to changes in choice preferences? Was the same snack name presented 10 times in a row or intermixed with other snack names? Please specify.

4) Also, please give more details on the wake control group. What did this group do while listening to the stimuli? Did they perform on a different task or did they activity attend the presented stimuli? Was EEG recorded during that time? If yes, please report also the results.

5) In general, the authors also need to make clear that they did not find any general effect of sleep on choice preferences in their study. Mechanistically, this is an important point. In the sleep and memory field, it is assumed that memories are spontaneously reactivated during sleep, and that TMR enhances this spontaneous mechanism. If choice preference depends on the degree of reactivation during sleep, then the change in WTP should be generally stronger after sleep as compared to wakefulness. (However, some results also in the sleep/memory domain suggest that general sleep associated benefits for memory are only visible after longer sleep periods, whereas TMR benefits are detectable already after short naps, see Diekelmann et al., 2012,). Thus, please discuss what general effects of sleep / spontaneous reactivations during sleep you would expect on choice preference and why. In other words, does memory consolidation during sleep generally affect decisions / preferences, or is TMR simply a way of (artificially) biasing a process that does not depend on sleep itself.

Essential revisions:

Both reviewers' concerns are listed separately but note that reviewer 1 comment 3 is very similar to comment 2 from reviewer 2 and reviewer 1 comment 2 is similar to comment 4 by reviewer 2: it is reasonable for you to collapse your responses to these concerns into two single responses.

Reviewer #1:

1) Were the subjects told they were going to be delivered verbal cues when asleep? Were they aware of the study hypotheses? What was the nature and results of the "post-sleep awareness test" – perhaps move this to the Results section.

We thank the reviewer for urging us to clarify this information. Critical to our experimental design, subjects were not informed about the study hypotheses and did not know that they were going to receive any external stimulation while asleep. Instead, the experiment was presented as a study aiming at measuring snack preferences with or without napping.

Second, at the end of the experiment, we administered a number of survey questions evaluating (i) the awareness of cue delivery during sleep and (ii) familiarity of snack items. The post-sleep awareness test was adopted from previous studies using similar TMR procedures (“Did you hear anything during sleep? Answer: Yes (please clarify)/No”. Rudoy et al., 2009; Antony et al., 2012; Ai et al., 2015), based on which 2 sleep group participants who reported having heard sounds were removed from data analyses.

Besides the awareness test, which was administered within the sleep cohort, the post-experiment survey also included questions for all participants assessing how familiar subjects were with each snack item and whether or not subjects had previously consumed each item. Results of these questions were largely consistent with findings of the pilot study based on which snacks were selected, and were included in statistical analyses testing the robustness of our findings (Supplementary file 1, Table 1B, Table 1C).

We have now included this additional information in Materials and methodssection.

2) What was the nature of recruiting so many participants whom regularly napped in the afternoon and duly cooperated and slept in a laboratory setting? This does not seem a typical population to me. How were the non-sleep group kept awake and were they checked for drowsiness during the word cueing?

Per editor suggestion, we combined question 2 from reviewer 1 and question 4 from reviewer 2, which are both about subjects in our study but from two distinct aspects: habitual nappers, and the experimental procedure for wake group subjects. We address each point in turn.

On the former, our choice of habitual nappers for the study was informed by (i) previous TMR studies, (ii) availability of these subjects, and (iii) experimental procedure specifically designed for these subjects.

First, our study, with subjects who regularly nap in the afternoon, is in keeping with previous TMR studies focusing on daytime naps. Creery et al. (2015), for example, recruited subjects “who anticipated that they would be able to sleep in the afternoon” and Ai et al., 2015 used subjects who “slept habitually for 1/2–2 h in the afternoon”. Prior studies with no restriction on napping habits often involve removing a significant number of participants due to the difficulty of these participants in falling asleep during the sleep phase of the experiment (e.g., Antony et al., 2012). To overcome this difficulty, previous research has also requested participants to sleep a few hours less than normal before coming to the experiment (Hauner et al., 2013). However, partial sleep restriction is likely to affect eating behavior on the subsequent day, including increasing caloric intake and self-reported hunger (Brondel et al., 2010). Importantly, such an effect is likely to interact with intrinsic food preferences and the caloric density of food options (Greer et al., 2013), therefore seriously interfering with the purpose of our study.

Another important reason for choosing habitual nappers in the current study is that daytime napping is not uncommon in China, especially among college students. According to a large-scale, cross-country survey on sleeping habits around the world (Soldatos et al., 2005), 33% of Chinese general population regularly naps in the afternoon, a percentage that is significantly higher than the average number around the world. Relative to the general population, it has been observed that college students are more likely to take a midday nap for preventing reduced vigilance in the afternoon (Milner and Cote, 2009), an impression that is supported by a study suggesting that 53% of undergraduate students in Australia reported frequent napping (Lovato et al., 2014).

In addition, as reviewer 1 pointed out, when focusing on habitual nappers in the experiment, it is of critical importance to make sure that our wake group participants do not fall asleep over the course of the stimulation session. During this 90-min period, the wake group subjects were allowed to engage in quiet activity including reading books or newspapers, watching documentary films, etc. Importantly, an experimenter accompanied these participants to ensure that they stayed awake throughout the period. In addition, using the Stanford Sleepiness Scale (Diekelmann et al., 2011), we assessed self-reported alertness for sleep and wake cohorts, and found no significant differences in vigilance after the 90-min period between two groups (sleep = 2.21 ± 0.16; wake = 2.76 ± 0.25; t = -1.84, P = 0.07; using a 7-point scale with 1 being very vigilant and 7 being very sleepy; Supplementary file 1, Table 1A, Table 1C).

We also thank reviewer 2 for urging us to clarify the experimental procedure related to the wake group. During the 90-min period, the wake group participants were allowed to engage in quiet activity including reading books or newspapers, watching documentary films, etc. When verbal cues were delivered, wake group participants were instructed to pause their activity and pay full attention to the stimuli. No other task was performed and no EEG was recorded during this period. We have now included this additional information in the Materials and methods

References:

Creery JD, Oudiette D, Antony JW, Paller KA (2015) Targeted memory reactivation during sleep depends on prior learning. Sleep 38:755-763.

Brondel L, Romer MA, Nougues PM, Touyarou P, Davenne D (2010) Acute partial sleep deprivation increases food intake in healthy men. Am J Clin Nutr 91:1550-1559.

Milner CE, Cote KA (2009) Benefits of napping in healthy adults: impact of nap length, time of day, age, and experience with napping. J Sleep Res 18:272-281.

Lovato N, Lack L, Wright H (2014) The napping behaviour of Australian university students. PLoS One 9:e113666.

3) What motivated the decision to multiply N2 by the mean δ and theta amplitudes? This sounds rather exploratory to me (given that each measure alone is close to significant; that several various combinations are possible; and that the final result (p=0.026) would not survive multiple comparisons.

Per editor suggestion, we combined questions from reviewer 1 and 2 that are both related to multiplying N2 length by δ activity for examining the neurophysiological correlate of the drift rate.

Inspired by Hu et al., 2015, in which the product of SWS and REM duration was used to predict the behavioral effect following TMR, we explored how δ activity and N2 duration could be combined to explain individual differences in post-sleep binary decisions. As the reviewers pointed out, this investigation was explorative in nature and not corrected for multiple corrections. Following reviewers’ suggestions, we have now removed it from the manuscript (also see the reply to point 1 of reviewer 2).

Reviewer #2:

1) As the general results of cueing during sleep appear to be rather robust, it puzzles me that the authors did not find any differences in the drift diffusion model. Is it possible to compare cued vs uncued items in this model, and not sleep vs. wake in general? How do the authors explain that this model is not sensitive for the observed choice preferences? Please discuss this point in more detail in the discussion. In particular, please elaborate on the functional consequences of the null findings in this model.

We thank the reviewer for raising this important point and the suggestion for investigating the difference between cued vs. uncued items, rather than sleep vs. wake groups, using established process models of choices. We address this issue by estimating binary decision data with another variant of sequential sampling model, the Linear Ballistic Accumulation (LBA) model. Unlike DDM, this model allows us to (i) directly compare the evidence accumulation processes between cued and uncued items, and (ii) perform a more stringent test on the latent cognitive components giving rise to decisions.

First, as reported in the previous submission, DDM makes specific assumptions about the underlying decision process that only one parameter, the initial bias, would reflect the potential difference between cued and uncued items. Other parameters, such as drift rate, decision threshold, and non-decision time, are assumed to be common or reflect a unified process for alternative options. According to our previous estimation, there was no significant bias either towards or against cued items in the initial drift point for both subject groups (sleep = 0.48 ± 0.04, P = 0.108, t-test for whether initial bias is different from.5, the value of zero bias; wake = 0.49 ± 0.04, P = 0.109, t-test for initial bias different from.5), suggesting symmetric decision criteria (aka response caution) for cued and uncued snacks.

Based on DDM alone, however, we cannot rule out the possibility that TMR affects other aspects of the post-sleep choice process that is not captured by the initial bias, such as the rate of evidence accumulation for cued items. Making such an inference would involve characterizing the drifting process for each available option separately, yet DDM assumes a single accumulator reflecting the relative value for competing options. We therefore used LBA, which, similar to DDM, is a popular sequential sampling model for characterizing choice and RT in a range of behavior including value-based choices (Brown and Heathcote, 2008; Donkin et al., 2011). Unlike DDM, however, LBA assigns separate accumulator for each available option racing towards a common decision threshold. When one accumulator reaches the threshold, the decision corresponding to that accumulator is made. Importantly, accumulators for alternative options are assumed to be independent, each governed by a different drift rate. This allows us to separately identify and directly compare drift rate estimates for items that have vs. have not been previously stimulated.

A second reason for adopting LBA is that a rigorous comparison between cued and uncued items would involve inferring latent cognitive components from overt behavior without using subjective preferences as model inputs. Ideally, preferences or factors related to preferences should be recovered from decision and RT, rather than entered into the model as observables. However, following prior studies on DDM in value-based decisions (e.g., Hutcherson et al., 2015; Polania et al., 2015), WTP2 was entered into DDM for computing trial-level value difference (i.e., DV = WTP2_cued – WTP2_uncued). LBA model, on the other hand, offers a simpler characterization of noise and a computationally more efficient likelihood function, which facilitates the parameter estimation based only on the data of choice and RT. This allows us to test whether the value of drift rate revealed solely based on choice and RT in the binary decision task would reproduce patterns of WTP elicited from the separate auction task.

Following prior studies (Brown and Heathcote, 2008), our LBA model included five parameters: two drift rates for cued and uncued items respectively, starting point variability, decision threshold, and non-decision time. Results based on a hierarchical Bayesian model estimation suggested that LBA explained observed patterns in RT and choice behavior very well: the observed choices and RT were highly correlated with predictions based on model estimation, with both correlation coefficients close to 1 (RT: r = 0.995, P = 2.2 × 10^-16; choices: r = 0.984, P = 2.2 × 10^-16; now included in Figure 4—figure supplement 2) and no significant difference in the explanatory power between sleep and wake groups (RT: F_1,88 = 0.42, P = 0.52; choices: F_1,88 = 0.50, P = 0.48). Comparing with DDM used in our previous submission, LBA provides better explanation for choice and RT as indicated by either WAIC (DDM = 3768.3, LBA = 3528.9) or Leave-one-out Cross-Validation (DDM = 3854.8, LBA= 3696.9).

Consistent with our hypothesis, drift rate estimates derived from LBA demonstrated a pattern similar to that of ΔWTP (as shown in Figure 2A). There was no main effect in the drift rate between sleep and wake groups (sleep = 3.17 ± 0.06; wake = 3.17 ± 0.07; main effect in treatment, F_1,90 = 0, P = 1), yet we found a significant within-subject difference between cued and uncued items, such that previously cued items were associated with higher drift rates (cued = 3.37 ± 0.06; uncued = 2.96 ± 0.06; main effect in cueing condition, F_1,90 = 25.26, P = 2.53×10^-6). Importantly, the cue-induced effect on drift rate varied between sleep and wake groups in a manner consistent with the pattern observed in ΔWTP: the drift rate was significantly higher for cued relative to uncued items in the sleep but not wake group ((cued vs. uncued) × (sleep vs. wake): F_1,90 = 5.29, P = 0.024).

Across subjects, the extent to which evidence accumulates faster for cued relative to uncued items can be predicted by the degree of preference bias toward cued items. Specifically, individuals with stronger biases towards cued items in WTP2 demonstrated a more pronounced increment in the drift rate from uncued to cued items (r = 0.37, P = 0.001; now included in Figure 4—figure supplement 4A), with no significant difference in the correlation coefficient across cohorts (sleep: r =.28; wake: r =.35; F = 0.50, P = 0.48). To validate these results, we performed posterior predictive simulation to assess, besides choice behavior, the extent to which drift rate variability was related to the variation in RT distributions (now included in Figure 4—figure supplement 5).

Interestingly, and consistent with results of DDM reported in the initial submission, the drift rate difference between cued and uncued items was significantly correlated with a neurophysiological measure we previously identified –– the product of N2 length by δ activity (r = 0.50, P = 0.015). Following reviewers’ suggestion, we no longer highlight this result in the main text (now included in Figure 4—figure supplement 4B).

Besides drift rate, LBA offers interesting findings on a new aspect of the choice process, which may further help to validate modeling results. Relative to the wake group, the sleep group was associated with a longer non-decision time (sleep = 0.72 ± 0.01; wake = 0.61 ± 0.01; t = 5.71, P = 7.23 × 10^-7), a parameter that captures individual differences in RT affected by cognitive factors unrelated to valuation (e.g., visual or motor processing). A likely reason for the swift non-decision process in the wake group is that subjects who stayed awake for 90 min felt hungrier than those who took a nap (post-stimulation self-reported hunger in the sleep group: 4.79 ± 0.29; wake group: 5.66 ± 0.26; t = – 2.26, P = 0.026). Consistent with this hypothesis, non-decision time estimates were scaled with the change of self-reported hunger before and after 90-min period at the across-subject level (r = 0.27, P = 0.009; Figure 4—figure supplement 4C), with no significant difference in the correlation coefficient between cohorts (sleep =.51; wake =.32; F = 0.23, P = 0.63). In contrast, there was no correlation between the non-decision time estimates and the change in vigilance, the familiarity of items, or demographic variables (all P > 0.5).

Together, these results support a multi-accumulator sequential sampling model, according to which the accumulation of evidence in support for previously cued options is accelerated by sleep TMR. More broadly, the above modeling results point to a possibility that memory that is reactivated during sleep renders higher quality evidence signals in favor of the associated item during the post-sleep choice process.

In addition, these results suggest that, whereas sleep slows down the cognitive processing unrelated to decisions, sleep likely does not exerts a general influence on parameters directly associated with value computation and comparison in our task, including drift rate, decision threshold, and starting-point variability. Notably, we found no significant main effect in LBA drift rate estimates between the sleep and wake group, paralleling our previous finding that the sleep/wake treatment does not affect DDM drift rates, and that sleep alone has no overall effect on choice preferences as indexed by WTP.

Compared with previous results based on DDM, we feel that the current LBA model provides a better description for the underlying cognitive processing and how such processing is influenced by TMR. We thus replace DDM with LBA in the main text of the revision, with all supporting figures and tables in the supplement (Figure 4—figure supplement 2-5; Supplementary file 1, Table1H).

References:

Donkin C, Brown S, Heathcote A, Wagenmakers EJ (2011) Diffusion versus linear ballistic accumulation: Different models but the same conclusions about psychological processes? Psychon Bull Rev 18:61-69.

Hutcherson, C.A., Bushong, B., Rangel, A., 2015. A Neurocomputational Model of Altruistic Choice and Its Implications. Neuron 87, 451–462.

Polania R, Moisa M, Opitz A, Grueschow M, Ruff CC (2015) The precision of value-based choices depends causally on fronto-parietal phase coupling. Nat Commun 6:8090.

2) Related to the point above, the reported correlation in Figure 4F (i.e., N2 amount*delta activity and drift rate) appears to be highly exploratory and speculative. With four model parameters and many possible combinations of sleep stage durations and oscillatory activity, it is highly probable that this is a false positive. Did the authors also correct for multiple comparisons here, as in the other analyses? I would strongly suggest omitting this part or at least strongly attenuating the conclusions made from this analysis. For me, the more important point here is that the model parameters are not sensitive for cueing benefits.

See response above to reviewer 1 comment 3.

3) The authors should give some more details on the reactivation itself: How many cues were delivered on average? How many cues were delivered in N2 vs N3 vs in other sleep stages? How does number of cues relate to changes in choice preferences? Was the same snack name presented 10 times in a row or intermixed with other snack names? Please specify.

We thank the reviewer for urging us to clarify information related to the TMR procedure used in the study. First, for each subject, 8 different snack names were presented in a randomized order. To increase the chance of triggering memory replay in the sleep group, each snack name was repeated for exactly 10 times in a row before presenting the next snack item. Because both the number of snack items presented during the stimulation session and the number of repetition for each snack were fixed, we are not able to investigate how different presentation schemes would result in differential influences on choice preferences.

Second, no verbal cues were steadily delivered during sleep stages other than N2. Following previous studies (Oudiette et al., 2013; Schreiner and Rasch, 2015), verbal cueing started 2 min after a sleep group subject displayed stable N2 sleep for the first time and immediately paused when EEG recordings showed any signs of micro-arousal, awakening, or changing of sleep stages. On average, the delivery of verbal cues during nap was interrupted 0.75 ± 0.58 times. Future studies with varying numbers of cue presentation and involving stimulation at different sleep stages will be needed to provide a more thorough understanding of how selectively memory reactivation plays a role in affecting choice preferences. We have now clarified this information and included the relevant experimental details in Materials and methods.

4) Also, please give more details on the wake control group. What did this group do while listening to the stimuli? Did they perform on a different task or did they activity attend the presented stimuli? Was EEG recorded during that time? If yes, please report also the results.

See above response to reviewer 1 comment 2.

5) In general, the authors also need to make clear that they did not find any general effect of sleep on choice preferences in their study. Mechanistically, this is an important point. In the sleep and memory field, it is assumed that memories are spontaneously reactivated during sleep, and that TMR enhances this spontaneous mechanism. If choice preference depends on the degree of reactivation during sleep, then the change in WTP should be generally stronger after sleep as compared to wakefulness. (However, some results also in the sleep/memory domain suggest that general sleep associated benefits for memory are only visible after longer sleep periods, whereas TMR benefits are detectable already after short naps, see Diekelmann et al., 2012). Thus, please discuss what general effects of sleep / spontaneous reactivations during sleep you would expect on choice preference and why. In other words, does memory consolidation during sleep generally affect decisions / preferences, or is TMR simply a way of (artificially) biasing a process that does not depend on sleep itself.

We thank the reviewer for this excellent comment regarding the underlying mechanism for preference enhancement. We have now made it clearer that in our experiment daytime nap is not associated with an overall preference change, and have discussed this finding in light of the active memory consolidation model in the discussion, replicated below.

“Our results also raise intriguing questions regarding whether the preference enhancement is specific to cued memory reactivation, or hold more generally for both cued and spontaneous reactivation during sleep. […] Future investigations using night sleep or animal models will be needed to assess whether and how spontaneous memory reactivation affects the storage of reward-related information and to determine whether sleep plays a general role in modulating goal-directed behavior through tuning reward evaluation.”

Author details

1. Sizhi Ai

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Department of Cardiology, Heart Center, Henan Key Laboratory of Neurorestoratology, The First Affiliated Hospital of Xinxiang Medical University, Weihui, China

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Writing—original draft, Writing—review and editing

   Contributed equally with

   Yunlu Yin

   Competing interests

   No competing interests declared

2. Yunlu Yin

   1. School of Psychological and Cognitive Sciences, Peking University, Beijing, China
   2. Faculty of Business and Economics, The University of Hong Kong, Hong Kong SAR, China

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Sizhi Ai

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-6433-3870

3. Yu Chen

   National Institute on Drug Dependence, Peking University, Beijing, China

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

4. Cong Wang

   1. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   2. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7997-4200

5. Yan Sun

   National Institute on Drug Dependence, Peking University, Beijing, China

   Contribution

   Conceptualization, Methodology

   Competing interests

   No competing interests declared

6. Xiangdong Tang

   Sleep Medicine Center, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China

   Contribution

   Validation, Writing—review and editing

   Competing interests

   No competing interests declared

7. Lin Lu

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   4. Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China

   Contribution

   Resources, Validation, Methodology

   Competing interests

   No competing interests declared

8. Lusha Zhu

   1. School of Psychological and Cognitive Sciences, Peking University, Beijing, China
   2. Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
   3. IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
   4. Key Laboratory of Machine Perception, Ministry of Education; Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   For correspondence

   lushazhu@pku.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8717-6356

9. Jie Shi

   1. National Institute on Drug Dependence, Peking University, Beijing, China
   2. Beijing Key Laboratory on Drug Dependence Research, Beijing, China
   3. The State Key Laboratory of Natural and Biomimetic Drugs, Beijing, China
   4. The Key Laboratory for Neuroscience of the Ministry of Education and Health, Peking University, Beijing, China

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Validation, Methodology, Project administration, Writing—review and editing

   For correspondence

   shijie@bjmu.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6567-8160

• 2,968

  Page views

• 449

  Downloads

• 7

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.